Glossary · Beginner
What Is a Prompt Set? Definition and Example
A prompt set is a fixed, versioned list of questions that you run in AI engines on a schedule to measure how your brand appears. It works like a keyword list in SEO, but with full questions written the way buyers ask assistants. Keeping it stable is what makes month-to-month comparisons meaningful.
What goes into a prompt set?
| Field | Example |
|---|---|
| ID | P014 |
| Prompt | "best payroll software for a 20-person company in Germany" |
| Intent | Compare |
| Tags | payroll, DE, SMB |
| Version added | v1.0 |
| Status | Active |
Illustrative row.
How do you build a good one?
- Collect real questions from sales, support, reviews and communities.
- Rewrite them as natural, complete questions.
- Balance intent: learn, compare and buy, plus a few branded accuracy prompts.
- Avoid leading prompts that name your brand in unbranded questions.
- Review with someone who talks to customers.
How do you maintain it?
- Freeze it for at least a quarter.
- Add new prompts as a new version; retire old ones without deleting them.
- Record the version in every report.
Example
Illustrative. A team's v1.0 set has 40 prompts. After a product launch, it creates v1.1 with 8 new prompts. Reports show v1.0 trends unchanged and v1.1 prompts tracked separately until they have their own history.
Why it matters for GEO
Every GEO metric is only as good as the prompts behind it. A careful, stable set turns noisy AI answers into a trend you can trust.
Common mistakes
- Copying short keywords from SEO tools instead of writing full questions.
- Including your brand name in prompts meant to test unbranded visibility.
- Letting different team members keep private versions of the set.
Related
Questions
How many prompts should a set have?
Many teams start with 30-50. Size depends on product lines, markets and how much you can act on.
Can I change the set?
Yes, through versions. Add prompts in a new version and keep the history, rather than editing old prompts.
Where do prompts come from?
Sales calls, support tickets, reviews, community threads and search query data, rewritten as natural questions.